Multi-objective VM Research Articles

Energy-Efficient and Load-Aware VM Placement in Cloud Data Centers

VM consolidation has been proposed as an effective solution to improve resource utilization and energy efficiency through VM migration. Improper VM placement during consolidation may cause frequent VM migrations and constant on–off switching of PMs, which can significantly hurt QoS and increase energy consumption. Most existing algorithms on efficient VM placement suffer the problem of easily falling into a sub-optimum prematurely since they are heuristic. Also, they do not achieve a good balance between multiple different goals, such as resource utilization, QoS, and energy efficiency. To address this problem, we propose an effective and efficient VM placement approach called MOEA/D-based VM placement, with the goal of optimizing energy efficiency and resource utilization. We develop an improved MOEA/D algorithm to search for a Pareto-compromise solution for VM placement. Our experiment results demonstrate that the proposed multi-objective optimization (MOO) model and VM placement solution have immense potential as it offers significant cost savings and a significant improvement in energy efficiency and resource utilization under dynamic workload scenarios.

GVMP: A multi-objective joint VM and vGPU placement heuristic for API remoting-based GPU virtualization and disaggregation in cloud data centers

The diverse needs of customers drive cloud providers to incorporate more GPU-enabled services. It is known that users barely utilize GPUs. Hence, GPU virtualization techniques are employed to enable GPU sharing and increase resource utilization. Among GPU virtualization techniques, API remoting leverages disaggregation for GPU provisioning, allowing GPUs to be accessed remotely by non-resident VMs. It enables a VM to access any available GPU in the data center which, in turn, provides more flexibility to reach a better placement. It also enables users to choose VM and vGPU instances separately based on computational needs as GPU-enabled VM instances with fixed configurations may not proportionally utilize allocated resources. However, an inefficient VM or vGPU placement may result in poor performance and increase in energy consumption and rejection ratio. There is also a network communication overhead for remotely allocated vGPUs. The main challenge is how to place VMs and vGPUs such that rejection ratio is decreased and the negative performance impacts caused by GPU sharing and remote access are kept at a minimum. In this work, we formally define the joint problem of VM and vGPU placement based on API remoting as a multi-objective ILP model and introduce gVMP as a heuristic to solve it. The proposed method is compared against state-of-the-art heuristics and commercial solutions that are based on API remoting and GRID technologies for the placement of GPU-enabled VMs. We study the effectiveness of the proposed gVMP algorithm for low and high arrival rates in slow and fast networks. Our results show that under different scenarios and in comparison to state-of-the-art policies, our heuristic improves average request duration, efficiency, and SLA by up to 40%, 25% and 29%, respectively.

Gvmp: A Multi-Objective Joint Vm and Vgpu Placement Heuristic for Api Remoting-Based Gpu Virtualization and Disaggregation in Cloud Data Centers

Gvmp: A Multi-Objective Joint Vm and Vgpu Placement Heuristic for Api Remoting-Based Gpu Virtualization and Disaggregation in Cloud Data Centers

Multi-Objective and Reserve-Based Vm Positioning in Multi-Cloud Environment

Multi-Objective and Reserve-Based Vm Positioning in Multi-Cloud Environment

ABSO: an energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment

ABSO: an energy-efficient multi-objective VM consolidation using adaptive beetle swarm optimization on cloud environment

VM reassignment in hybrid clouds for large decentralised companies: A multi-objective challenge

Optimising the data centres of large IT organisations is complex as (i) they are composed of various hosting departments with their own preferences and (ii) reassignment solutions can be evaluated from various independent dimensions. But in reality, the problem is even more challenging as companies can now choose from a pool of cloud services to host some of their workloads. This hybrid search space seems intractable, as each workload placement decision (seen as running in a virtual machine on a server) is required to answer many questions: can we host it internally? In which hosting department? Are the capital allocators of this hosting department ok with this placement? How much does it save us and is it safe? Is there a better option in the Cloud? Etc. In this paper, we define the multi-objective VM reassignment problem for hybrid and decentralised data centres. We also propose H2–D2, a solution that uses a multi-layer architecture and a metaheuristic algorithm to suggest reassignment solutions that are evaluated by the various hosting departments (according to their preferences). We compare H2–D2 against state-of-the-art multi-objective algorithms and find that H2–D2 outperforms them both in terms of quantity (approx 30% more than the second-best algorithm on average) and quality of solutions (19% better than the second-best on average).

Exact and Hybrid Solutions for the Multi-Objective VM Reassignment Problem

Machine Reassignment is a challenging problem for constraint programming (CP) and mixed integer linear programming (MILP) approaches, especially given the size of data centres. Hybrid solutions mixing CP and heuristic algorithms, such as, large neighbourhood search (CBLNS), also struggle to address the problem given its size and number of constraints. The multi-objective version of the Machine Reassignment Problem is even more challenging and it seems unlikely for CP, MILP or hybrid solutions to obtain good results in this context. As a result, the first approaches to address this problem have been based on other optimisation methods, including metaheuristics. In this paper we study three things: (i) under which conditions a mixed integer optimisation solver, such as IBM ILOG CPLEX, can be used for the Multi-objective Machine Reassignment Problem; (ii) how much of the search space can a well-known hybrid method such as CBLNS explore; and (iii) can we find a better hybrid approach combining MILP or CBLNS and an- other recent metaheuristic proposed for the problem (GeNePi). We show that MILP can handle only small or medium scale data centres, and with some relaxations, such as, an optimality tolerance gap and a limited number of directions explored in the search space. CBLNS on the other hand struggles with the problem in general but achieves reasonable performance for large instances of the problem. However, we show that our hybridisation improves both the quality of the set of solutions (CPLEX+GeNePi and CBLNS+GeNePi improve the solutions by +17.8% against CPLEX alone and +615% against CBLNS alone) and number of solutions (8.9 times more solutions than CPLEX alone and 56.76 times more solutions than CBLNS alone), while the processing time of CPLEX+GeNePi and CBLNS+GeNePi increases only by 6% and 16.4% respectively. Overall, the study shows that CPLEX+GeNePi is the best algorithm for small instances (CBLNS+GeNePi only gets 45.2% of CPLEX+GeNePi’s hypervolume) while CBLNS+GeNePi is better than the others on large instances (that CPLEX+GeNePi cannot address).

The Multi-objective VM Resource Scheduling by Using an Improved PSO Algorithm in the Cloud Data Center

The Multi-objective VM Resource Scheduling by Using an Improved PSO Algorithm in the Cloud Data Center