Bayllocator: a proactive system to predict server utilization and dynamically allocate memory resources using Bayesian networks and ballooning

Many embedded systems feature processors coupled with a small and fast scratchpad memory. To the difference with caches, allocation of data to scratchpad memory must be handled by software. The major gain is to enhance the predictability of memory accesses latencies. A compile-time dynamic allocation approach enables eviction and placement of data to the scratchpad memory at runtime. Previous dynamic scratchpad memory allocation approaches aimed to reduce average-case program execution time or the energy consumption due to memory accesses. For real-time systems, worst-case execution time is the main metric to optimize. In this paper, we propose a WCET-directed algorithm to dynamically allocate static data and stack data of a program to scratchpad memory. The granularity of placement of memory transfers (e.g. on function, basic block boundaries) is discussed from the perspective of its computation complexity and the quality of allocation.

Purpose – The physical server transition to virtualized infrastructure server have encountered crucial problems such as server consolidation, virtual machine (VM) performance, workload density, total cost of ownership (TCO), and return on investments (ROIs). In order to solve the problems mentioned above, the purpose of this paper is to perform the analysis of virtualized cloud server together with shared storage as well as the estimation of consolidation ratio and TCO/ROI in server virtualization. Design/methodology/approach – This paper introduces five distinct virtualized cloud computing servers (VCCSs), and provides the appropriate assessment to five well-known hypervisors built in VCCSs. The methodology the authors proposed in this paper will gives people an insight into the problem of physical server transition to virtualized infrastructure server. Findings – As a matter of fact, VM performance seems almost to achieve the same level, but the estimation of VM density and TCO/ROI are totally different among hypervisors. As a result, the authors have the recommendation to choose the hypervisor ESX server if you need a scheme with higher ROI and lower TCO. Alternatively, Proxmox VE would be the second choice if you like to save the initial investment at first and own a pretty well management interface at console. Research limitations/implications – In the performance analysis, instead of ESX 5.0, the authors adopted ESXi 5.0 that is free software, its function is limited, and does not have the full functionality of ESX server, such as: distributed resource scheduling, high availability, consolidated backup, fault tolerance, and disaster recovery. Moreover, this paper do not discuss the security problem on VCCS which is related to access control and cryptograph in VMs to be explored in the further work. Practical implications – In the process of virtualizing the network, ESX/ESXi has restrictions on the brand of the physical network card, only certain network cards can be detected by the VM. For instance: Intel and Broadcom network cards. The newer versions of ESXi 5.0.0 and above now support parts of Realtek series (Realtek 8186, Realtek 8169, and Realtek 8111E). Originality/value – How to precisely assess the hypervisor for server/desktop virtualization is also of hard question needed to deal with it crisply before deploying new IT with VCCS on site. The authors have utilized the VMware calculator and developed an approach to server/desktop consolidation, virtualization performance, VM density, TCO, and ROIs. As a result, in this paper the authors conducted a comprehensive approach to analyze five well-known hypervisors and will give the recommendation for IT manager to choose a right solution for server virtualization.

Modern applications running on cloud data centers often consume a large amount of memory and their memory demands can vary during execution. Dynamic memory allocation is a necessity for high memory utilization. For a large dataset application, using hugepages instead of regular 4KB pages can efficiently reduce memory access and memory management overhead and improve overall performance. Virtualization, which is widely applied in data centers for server consolidation, brings new challenges to manage memory dynamically and effectively, especially for hugepages. In a virtualized system, ballooning is a popular mechanism used to dynamically adjust memory allocations for co-located virtual machines. We observe that the current Linux Kernel-Based Virtual Machine (KVM) does not support huge page ballooning. An application that can benefit from hugepages often loses its performance advantage when the guest OS experiences memory ballooning. This paper presents design and implementation of HUB, a HUgepage Ballooning mechanism in KVM which can dispatch memory in the granularity of hugepages. The experimental results show that our approach significantly reduces TLB misses and improves the overall performance for those applications with large memory demand.

Virtual machine (VM) migration in the cloud computing (CC) environment is an important issue to solve many problems, such as: Load Balancing, accomplished by migrating VMs out of overloaded/overheated servers, and Server Consolidation, where servers can be selectively brought down for maintenance after migrating their workload to other servers. In this paper, we propose an efficient VM migration algorithm based on minimization of migrations in CC to improve efficiency and response the requirements for user and restrict of service level agreements (SLA) violation. Experimental results showed the effectiveness of the proposed algorithm compared with existing algorithm.

Recent compilers allow a general-purpose program (written in a conventional programming language) that handles private data to be translated into a secure distributed implementation of the corresponding functionality. The resulting program is then guaranteed to provably protect private data using secure multi-party computation techniques. The goals of such compilers are generality, usability, and efficiency, but the complete set of features of a modern programming language has not been supported to date by the existing compilers. In particular, recent compilers PICCO and the two-party ANSI C compiler strive to translate any C program into its secure multi-party implementation, but they currently lack support for pointers and dynamic memory allocation, which are important components of many C programs. In this work, we mitigate the limitation and add support for pointers to private data and consequently dynamic memory allocation to the PICCO compiler, enabling it to handle a more diverse set of programs over private data. Because doing so opens up a new design space, we investigate the use of pointers to private data (with known as well as private locations stored in them) in programs and report our findings. Aside from dynamic memory allocation, we examine other important topics associated with common pointer use such as reference by pointer/address, casting, and building various data structures in the context of secure multi-party computation. This results in enabling the compiler to automatically translate a user program that uses pointers to private data into its distributed implementation that provably protects private data throughout the computation. We empirically evaluate the constructions and report on the performance of representative programs.

Dynamic memory allocators are critical components of modern systems, and developers strive to find a balance between their performance and their security. Unfortunately, vulnerable allocators are routinely abused as building blocks in complex exploitation chains. Most of the research regarding memory allocators focuses on popular and standardized heap libraries, generally used by high-end devices such as desktop systems and servers. However, dynamic memory allocators are also extensively used in embedded systems but they have not received much scrutiny from the security community.In embedded systems, a raw firmware image is often the only available piece of information, and finding heap vulnerabilities is a manual and tedious process. First of all, recognizing a memory allocator library among thousands of stripped firmware functions can quickly become a daunting task. Moreover, emulating firmware functions to test for heap vulnerabilities comes with its own set of challenges, related, but not limited, to the re-hosting problem.To fill this gap, in this paper we present HEAPSTER, a system that automatically identifies the heap library used by a monolithic firmware image, and tests its security with symbolic execution and bounded model checking. We evaluate HEAPSTER on a dataset of 20 synthetic monolithic firmware images — used as ground truth for our analyses — and also on a dataset of 799 monolithic firmware images collected in the wild and used in real-world devices. Across these datasets, our tool identified 11 different heap management library (HML) families containing a total of 48 different variations. The security testing performed by HEAPSTER found that all the identified variants are vulnerable to at least one critical heap vulnerability. The results presented in this paper show a clear pattern of poor security standards, and raise some concerns over the security of dynamic memory allocators employed by IoT devices.

Frequent dynamic memory allocations (DyMAs) can significantly hinder the scalability of parallel multi-threaded programs. As the number of threads grows, DyMAs can even become the main performance bottleneck. We introduce modern tools and methods for evaluating the impact of DyMAs and present techniques for its reduction, which include scalable heap implementations, small buffer optimization, and memory pooling. Additionally, we provide a survey of state-of-the-art implementations of these techniques and study them experimentally by using a benchmark program, server simulator software, and a real-world high-performance computing application. As a result, we show that relatively small modifications in parallel program's source code or a way of its execution may substantially reduce the runtime overhead associated with the use of dynamic data structures.

Recently there has been increasing interest in supporting execution of Java Virtual Machine (JVM) applications on accelerator architectures, such as GPUs. Unfortunately, there is a large gap between the features of the JVM and those commonly supported by accelerators. Examples of important JVM features include exceptions, dynamic memory allocation, use of arbitrary composite objects, file I/O, and more. Recent work from our research group tackled the first feature in that list, JVM exception semantics[14]. This paper continues along that path by enabling the acceleration of JVM parallel regions that include object references and dynamic memory allocation.

Energy efficiency is a major issue in Cloud computing infrastructure. The large power consumption is mainly attributed to the large number of modern data centers operating within. Developing these data centers includes dynamically expanding their infrastructures to meet the ever-increasing demand for huge computation, large storage, and massive communication. Energy conservation through optimization of resources and management policies in the Cloud are a viable solution. Using virtualization to save power and employing such practices as using Virtual Machines (VMs), Server Consolidation, and VM Live Migration. This paper investigates the opportunities for Green Cloud Computing (GCC) to obtain a more comprehensive prospect towards achieving energy efficient Cloud Computing, and presents an energy efficient network resources management approach in an Infrastructure as a Service (IaaS) Cloud model. We focus on developing an energy efficient algorithm by proposing a practical multi-level Cloud Resource-Network Management (CRNM) algorithm, which is implemented in a virtual Cloud environment using Snooze framework as the Cloud energy efficiency manager. The optimization focuses on the utilization of network bandwidth as main resource under test, while also taking into account the other resources, such as CPU and memory, to get the desired performance. We choose a fat tree topology as a common three tier architecture for Cloud data canters. We conclude that our proposed algorithm will save up to 75% of power consumption in Cloud data centers, with an observed increase in efficiency compared to Non-Power Aware (NPA), Power aware(PA), and Greedy algorithms, where network elements consume about 30% of the total power of Cloud data centers.

Nowadays, cloud computing and virtualization are two major study areas. Unlike in the past, virtualization is being used by a growing variety of organizations for power reduction, testing and development, disaster recovery, server consolidation, dynamic load balancing, improved system reliability, and security. It also offers high availability for critical applications and expedites their deployment and migration. Through cloud computing, information technology resources can be provided to end users as services via the Internet. Virtualization is one of these crucial cloud computing core technologies. We provide a thorough overview of virtualization in this paper.

The end of the 20th century and the advent of the new millennium have brought in a true merger of sciences for the benefit of mankind. The biggest promise it holds is that of improving the quality of human life by the discovery of newer medicines and better cures for diseases such as cancer and heart disease. Pharmaceutical companies and academic institutions alike have not failed to deliver on part of the promise by bringing out technologies and products that have significantly decreased mortality and morbidity associated with these diseases. An increase in the scale and complexity of the technologies has made it increasingly important to develop intelligent tools to analyze their output, and numerous mathematical and statistical techniques have been explored and exploited to do exactly this. Bayesian networks (BN) and similar graphical models for multivariate analysis are being used for analyzing these data with great success. They have made possible a high resolution insight into disease mechanisms like never before. These insights into the biological processes of health and disease have helped identify the appropriate targets for drug discovery and aided in the process of bringing better drugs faster to the market for patients in need. This chapter briefly explains the application and contribution of Bayesian networks to the drug discovery and development process.

Purpose This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms. Design/methodology/approach This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks. Findings The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments. Research limitations/implications The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available. Practical implications The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate. Originality/value Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

Bayesian belief networks for IR

We study the semantic meaning of block structure using game semantics and introduce the notion of block-innocent strategies, which turns out to characterise call-by-value computation with block-allocated storage through soundness, finitary definability and universality results. This puts us in a good position to conduct a comparative study of purely functional computation, computation with block storage and dynamic memory allocation respectively. For example, we show that dynamic variable allocation can be replaced with block-allocated variables exactly when the term involved (open or closed) is of base type and that block-allocated storage can be replaced with purely functional computation when types of order two are involved. To illustrate the restrictive nature of block structure further, we prove a decidability result for a finitary fragment of call-by-value Idealized Algol for which it is known that allowing for dynamic memory allocation leads to undecidability.

Free to read on publisher's website Utilizing dynamic resource allocation for load balancing is considered as an important optimization process in cloud computing. In order to achieve maximum resource efficiency and scalability in a speedy manner this process is concerned with multiple objectives for an effective distribution of loads among virtual machines. In this realm,exploring new algorithms, as well as development of novel algorithms, is highly desired for technological advancement and continued progress in resource allocation application in cloud computing. Accordingly, this paper explores the application of two relatively new optimization algorithms and further proposes a hybrid algorithm for load balancing which can contribute well in maximizing the throughput of the cloud provider's network. The proposed algorithm is a hybrid of teaching-learning-based optimization algorithm (TLBO) and grey wolves optimization algorithm (GW). The hybrid algorithm performs more efficiently than utilizing every single one of these algorithms. Furthermore, it well balances the priorities and effectively considers load balancing based on time, cost, and avoidance of local optimum traps, which consequently leads to minimal amount of waiting time. To evaluate the effectiveness of the proposed algorithm, a comparison with the TLBO and GW algorithms is conducted and the experimental results are presented.