Revenue Maximization in Cloud Federation Based on Multi-Choice Multidimensional Knapsack Problem

Cloud Federation offers Cloud Service Providers (CSPs) the ability to lease excess capacity to other CSPs in need of temporary additional resources such as large amounts of computational and networking power, or storage capacity to meet consumer's insatiable demand requirements. This promise, not only directly contradicts the Cloud Computing principles of scalability and elasticity, but also challenges and undermines the main business interest of CSPs in competition for unrivaled market positioning and industry dominance. In this paper, we provide a new view on Cloud Federation through analyzing its technological and organizational integration challenges and identifying the business incentives required for CSPs to combine their aggregated infrastructures to meet greater business outcomes and increased sustainable profits. Our position is that the Internet of Things (IoT) will be the main business driver and catalyst for Cloud Federation.

In Cloud computing, resources such as CPU, RAM, and disk etc. are provided as a service via the Internet. Elasticity is a key feature of Cloud Computing. It aims to enabling a Cloud Service Provider (CSP) to negotiate the possibility to borrow external resources from other CSPs when its own facilities are not able to satisfy a client request. Reciprocally, a CSP may sell some of its unused resources to another CSP in case of under load. Today, Cloud Brokering and Cloud Federation are the two approaches considered for Cloud elasticity. In the context of the Easi-Clouds European ITEA 2 research project, we aim to develop Pricing-as-a-Service (PraaS) suited to Federated Cloud environment. The aim of this paper is twofold. First, we provide a state of the art of pricing in multi-cloud environment. We discuss specifications of Cloud Brokering and Cloud Federation. Three types of pricing strategies (on-demand, spot and reserved) are presented with different existing resource pricing models. In the second part we propose different pricing policies suited to the Federated environment with numerical analysis via simulation. Finally, results are provided showing the best pricing policy suited for federation cloud as well as the spot influence on a federated environment. Results are evaluated in term of CSPs revenues and the number of rejected sessions.

With the rapid development of e-commerce over Internet, web services have attracted much attention in recent years. Nowadays, enterprises are able to outsource their internal business processes as services and make them accessible via the Web. Then they can dynamically combine individual services to provide new value-added services. With the increasing number of web services having equivalent functionality, the binding procedure is driven by some non-functional, Quality of Service (QoS) criteria, such as the money cost, response time, reputation, reliability or a trade-off between them. Thus, an important problem is, given QoS constraints, how to aggregate and leverage individual service’s QoS information to derive the optimal QoS of the composite service. In this paper, we propose a novel QoS model for performing flexible service selection. The key idea of the model is to relax users’ QoS constraints and try to find the most possible services satisfying users’ QoS requirements. Based on the proposed QoS framework, we develop various algorithms for making service selection on individual and composite services. We also introduce a top-k ranking strategy to reflect a user’s personalized requirements. Experimental evaluation shows the proposed QoS model is efficient and practical.

By leveraging the Internet, cloud computing allows users to have on-demand access to large pools of configurable computing resources. PaaS (Platform as a Service), IaaS (Infrastructure as a Service), and SaaS (Software as a Service) are three basic categories for the services provided by cloud the computing environments. Quality of service (QoS) metrics like reliability, availability, performance, and cost determine which resources and services are available in a cloud computing scenario. Provider and the user-specified performance characteristics, such as, rejection rate, throughput, response time, financial cost, and energy consumption, form the basis for QoS. To fulfil the needs of its customers, cloud computing must ensure that its services are given with the appropriate quality of service QoS. A “A legally enforceable agreement known as a “Service Level Agreement” (SLA) between a service provider and a customer that outlines service objectives, quality of service requirements, and any associated financial penalties for falling short. We, therefore, presented “A Proactive Resource Supply based Run-time Monitoring of SLA in Cloud Computing”, which allows for the proactive management of SLAs during run-time via the provisioning of cloud services and resources. Within the framework of the proposed work, SLAs are negotiated between cloud users and providers at run-time utilizing SLA Manager. Resources are proactively allocated via the Resource Manager to cut down on SLA violations and misdetection costs. As metrics of performance, we looked at the frequency with which SLAs were broken and the money lost due to false positives. We compared the proposed PRP-RM-SLA model’s simulated performance to the popular existing SLA-based allocation strategy SCOOTER. According to simulation data, the suggested PRP-RM-SLA model is 25% more effective than the current work SCOOTER at reducing SLA breaches and the cost of misdetection.

Cloud Service Providers (CSPs) offer a wide variety of scalable, flexible, and cost-efficient services to cloud users on demand and pay-per-utilization basis. However, vast diversity in available cloud service providers leads to numerous challenges for users to determine and select the best suitable service. Also, sometimes users need to hire the required services from multiple CSPs which introduce difficulties in managing interfaces, accounts, security, supports, and Service Level Agreements (SLAs). To circumvent such problems having a Cloud Service Broker (CSB) be aware of service offerings and users Quality of Service (QoS) requirements will benefit both the CSPs as well as users. In this work, we proposed a Fuzzy Rough Set based Cloud Service Brokerage Architecture, which is responsible for ranking and selecting services based on users QoS requirements, and finally monitor the service execution. We have used the fuzzy rough set technique for dimension reduction. Used weighted Euclidean distance to rank the CSPs. To prioritize user QoS request, we intended to use user assign weights, also incorporated system assigned weights to give the relative importance to QoS attributes. We compared the proposed ranking technique with an existing method based on the system response time. The case study experiment results show that the proposed approach is scalable, resilience, and produce better results with less searching time.

A service level agreement (SLA) is part of a contract between a cloud service provider (CSP) and a cloud service user (CSU) which defines and describes different agreed properties of the service. The negotiation of the SLA between a CSU and a CSP is a pivotal process in the SLA life cycle. An SLA may include many negotiable quality of service (QoS) parameters with each QoS parameter having multiple acceptable values. Different combinations of acceptable values for all QoS parameters grow exponentially as number of QoS parameters and their selectable values increase. Manually negotiating for all of the QoS parameters considering their various options can be an erroneous and diligent task with lower utility. In this paper, a time efficient flip-flop negotiation strategy for the SLAs in cloud services is presented. The flip-flop negotiation strategy is based on a time dependent 3D utility function and on a dynamic concession strategy using the polynomial extrapolation of opponent's concession. This negotiation strategy is useful for time-critical cloud services as it operates with a motive of reaching an agreement at earlier time without compromising over the utility level of the agreement. Moreover, a multi-provider concurrent negotiation method is presented in this work using the flip-flop negotiation strategy. Multiple experiments are performed (using the implementation of flip-flop negotiation strategy) to test the presented work. The results of the experiments enforce the validity of the flip-flop negotiation strategy for such cloud services where time is an important factor during the SLA negotiation process.

In this paper, we have proposed a novel algorithm based on Ant Colony Optimization (ACO) for finding near-optimal solutions for the Multi-dimensional Multi-choice Knapsack Problem (MMKP). MMKP is a discrete optimization problem, which is a variant of the classical 0-1 Knapsack Problem and is also an NP-hard problem. Due to its high computational complexity, exact solutions of MMKP are not suitable for most real-time decision-making applications e.g. QoS and Admission Control for Adaptive Multimedia Systems, Service Level Agreement (SLA) etc. Although ACO algorithms are known to have scalability and slow convergence issues, here we have augmented the traditional ACO algorithm with a unique random local search, which not only produces near-optimal solutions but also greatly enhances convergence speed. A comparative analysis with other state-of-the-art heuristic algorithms based on public MMKP dataset shows that, in all cases our approaches outperform others. We have also shown that our algorithms find near optimal (within 3% of the optimal value) solutions within milliseconds, which makes our approach very attractive for large scale real time systems.

Multi objective trust aware task scheduling algorithm in cloud computing using whale optimization

Cloud Computing is known as a provider of dynamic services. It utilizes a very large, scalable and virtualized resource over the Internet. So many industries have joined this bandwagon nowadays. One of the major research issues is to maintain good Quality of Service (QoS) of a Cloud Service Provider (CSP). The QoS encompasses different parameters, like, smart job allocation strategy, efficient load balancing, response time optimization, reduction in wastage of bandwidth, accountability of the overall system, etc. The efficient allocation strategy of the independent computational jobs among different Virtual Machines (VM) in a Datacenter (DC) is a distinguishable challenge in the Cloud Computing domain and finding out an optimal job allocation strategy guided by a good scheduling heuristic for such an environment is an NP-complete problem. So different heuristic approaches may be used for better result. The related works with other NP- complete problems have shown that solutions guided by heuristic approaches can often be improved by applying local scheduling procedure for allocating independent jobs in the virtual machines (VM) inside a Datacenter (DC), which, when combined with fast construction heuristics, can find out the shorter schedules on benchmark problems than the other solution methodologies found in different literatures, and in significant less time. This paper highlights a smart job allocation strategy for a CSP by applying Round-Robin (RR) scheduling policy.

This paper presents an Intelligent Cloud Resource Allocation Service (ICRAS) that assists consumers with the complex task of finding the optimal configuration of Cloud resources given a consumer’s specific needs. The process of selecting a CSP becomes increasingly complex as the number of Cloud Service Providers (CSP) offering similar services continues to grow. Consumers can pick and choose between CSPs based on a growing number of options, including price, Quality of Service, reputation and so forth. The advent of dynamic pricing (based on real-time availability) further increases the complexity of CSP selection. ICRAS alleviates much of this burden from the consumer by automating the processes of service discovery, evaluation, negotiation and migration. Furthermore, ICRAS monitors Service Level Agreement (SLA) compliance using non-repudiable monitoring techniques.

The various services that are offered by IoT and Cloud Service Providers (CSPs) to the customers today feature a pay-per-use service-charging policy. Customers can choose and avail these services when they want, how they want, and from where they want on demand. Demand for these services has increased drastically over the years among individuals and enterprises worldwide, and thus, it is very important to keep up good Quality of Service (QoS). This chapter highlights the history of internet, the gradual evolution of cloud computing, the reasons behind it, evolution and concepts of the Internet of Things (IoT), CloudIoT and its necessities, and various applications and service fields of CloudIoT. This chapter also precisely highlights various concepts regarding maintenance of good QoS, controversies in QoS maintenance, different parameters that the QoS depends on, various problems faced in maintaining those parameters, and the possible solutions for overcoming those problems. Possible directions towards future works are also highlighted in this chapter.

Configurable processes are increasingly being adopted by enterprises that seek experience sharing and best practice adoption. A configurable process is a customizable model that specifies how different enterprises perform similar processes. At the modeling level, a configurable process model provides for flexible business process (BP) reuse by (de)selecting the (ir)relevant parts to derive a particular process variant. At the exploitation level, it offers flexibility and agility to an enterprise looking to outsource its BP to different providers cooperating in a cloud federation. More specifically, an enterprise can use a configurable process model to derive particular process variants that it outsources depending on its objectives. In particular, it may opt for outsourcing the variant that results in the optimal deployment, e.g., having the minimal cost of allocated cloud services that fulfill the user quality of service (QoS) requirements. However, identifying the optimal deployment variants is a complex problem because of the heterogeneity of services within a cloud federation and the number of possible variants that can be derived from a configurable process model. In addition, the complexity of this problem increases for variable user QoS requirements. In this paper, we propose an approach to derive, from a configurable process model, the variant that has the optimal deployment in a cloud federation. We propose a linear programming approach that accounts for the variability of both the BP model and the user QoS requirements, and that ensures an optimal time-aware cloud service allocation. We experimentally show the effectiveness and flexibility of our approach on a generated testbed.

Server placement in mobile cloud computing: A comprehensive survey for edge computing, fog computing and cloudlet

In this paper, using the concept of capacity regions, an algorithm for joint rate allocation and routing scheme is proposed in order to maximize the total throughput of multihop cellular CDMA networks. The notion of infeasibility factor is defined and used to propose an adaptive scheme on top of the above algorithm to manage fundamental coverage-capacity tradeoff for the downlink. In fact, the output of our algorithm is the joint rate allocation, routing scheme and base station (relay) assignment which in conjunction with the proposed adaptive scheme leads to implementing the concept of cell breathing via allocating proper transmit powers to base stations and relays. We show that our optimization problem can be mapped to a multidimensional multichoice knapsack problem (MMKP). Since MMKP is NP-hard, a polynomial-time heuristic algorithm is then developed to solve our MMKP.

Data sharing is important in the medical domain. Sharing data allows large-scale analysis with many data sources to provide more accurate results (especially in the case of rare diseases with small local datasets). Cloud federations can leverage sharing medical data stored in different cloud platforms, such as Amazon, Microsoft, Google Cloud, etc. They also enable access to distributed data of patients. The pay-as-yougo model in cloud federations raises important issues of Multi-Objective Optimization Problems (MOOP) related to users’ preferences, such as response time, money, quality, etc. However, optimizing a query in a cloud federation is complex with increasing the variety, especially due to a wide range of communications and pricing models. The variety of virtual machines configuration also leverages the high complexity in generating the space of candidate solutions. Indeed, in such a context, it is difficult to provide accurate estimations and optimal solutions to make relevant decisions. The first challenge is how to estimate accurate parameter values for MOOPs without precise knowledge of the execution environment in a cloud federation consisting of different sites. To address the accurate estimation of parameter values problem, we present the Dynamic Regression Algorithm (DREAM), which can provide accurate estimations in a cloud federation with limited historical data. DREAM focuses on reducing the size of historical data while maintaining the estimation accuracy. The second challenge is how to find an approximate optimal solution in MOOPs using an efficient Multi-Objective Optimization algorithm. To address the problem of finding an approximate optimal solution, we present Non-dominated Sorting Genetic Algorithms based on Grid partitioning (NSGA-G) for MOOPs. The proposed algorithm is integrated into the Intelligent Resource Scheduler, a solution for heterogeneous databases, to solve MOQP in cloud federations. We validate our algorithms with experiments on a decision support benchmark (TPC-H benchmark).