Distributed computing, in which a resource-intensive task is divided into subtasks and distributed among different machines, plays a key role in solving large-scale problems.Coded computingis a recently emerging paradigm where redundancy for distributed computing is introduced to alleviate the impact of slow machines (stragglers) on the completion time. We investigate coded computing solutions over elastic resources, where the set of available machines may change in the middle of the computation. This is motivated by recently available services in the cloud computing industry (e.g., EC2 Spot, Azure Batch) where low-priority virtual machines are offered at a fraction of the price of the on- demand instances but can be preempted on short notice. Our contributions are three-fold. We first introduce a new concept calledtransition wastethat quantifies the number of tasks existing machines must abandon or take over when a machine joins/leaves. We then develop an efficient method to minimize the transition waste for the cyclic task allocation scheme recently proposed in the literature (Yang et al. ISIT’19). Finally, we establish a novel solution based on finite geometry achievingzerotransition wastes given that the number of active machines varies within a fixed range.

Motivated by recently available services in the cloud computing industry, e.g., EC2 Spot or Azure Batch, where spare/low-priority virtual machines are offered at a fraction of the price of the on-demand instances but can be preempted on short notice, we investigate coded computing solutions over elastic resources, where the set of available machines may change in the middle of the computation. Our contributions are two-fold: We first propose an efficient method to minimize the transition waste, a newly introduced concept quantifying the total number of tasks that existing machines have to abandon or take on anew when a machine joins or leaves, for the cyclic elastic task allocation scheme recently proposed in the literature (Yang et al. ISIT'19). We then proceed to generalize such a scheme and introduce new task allocation schemes based on finite geometry that achieve zero transition wastes as long as the number of active machines varies within a fixed range. The proposed solutions can be applied on top of existing coded computing schemes tolerating stragglers.

Dynamic virtual machine (VM) consolidation is considered an effective approach for improving power consumption and computing resource utilization in cloud-based data centers. However, the ever-changing workload in a data center makes it difficult for VM consolidation to prevent service level agreement (SLA) violations and optimize power consumption. The detection of overutilized and underutilized physical machines (PMs) plays a significant role in effective VM consolidation, immediately improving resource utilization, SLA violations, and power consumption. This paper proposes the dynamic threshold-based fuzzy approach (DTFA) for detecting overloaded and underutilized PMs, and the Lowest Interdependence Factor Exponent Multiple Resources Predictive (LIFE-MP) approach for VM placement, by considering multiple computing resources being used simultaneously in a cloud environment. The DTFA is a fuzzy threshold-based approach used to adjust the threshold values of PMs in a cloud environment. The LIFE-MP approach selects a PM at which to place the migrating VM, based on the PM with the lowest correlation coefficient value among the already-running VMs and the migrating VM to reduce performance degradation because of the VM migration. The comparison between LIFE-MP scheme and a power-aware best-fit decreasing (PABFD) scheme shows that the proposed scheme reduces power consumption by 22.52, SLA violations by 45.63, and the number of VM migrations by 56.68.

Dynamic virtual machine (VM) consolidation is considered an effective approach for improving power consumption and computing resource utilization in cloud-based data centers. However, the ever-changing workload in a data center makes it difficult for VM consolidation to prevent service level agreement (SLA) violations and optimize power consumption. Detection of overutilized and underutilized physical machines (PMs) plays a significant role in effective VM consolidation, immediately improving resource utilization, SLA violations, and power consumption. This paper presents a new proposal for the dynamic adjustment of threshold values that aims to minimize the number of migrations in varying workload environments. The proposed approach, named the `dynamic threshold-based fuzzy approach' (DTFA), is a fuzzy threshold-based approach used for adjusting the threshold values of PMs in a cloud environment. The proposed approach allows the number of migrations caused by overloading to be reduced and SLAs to be met. Three sets of experiments with different workloads were conducted to validate the proposed approach. The results demonstrate that DTFA outperforms existing solutions by an average of 22.52%, 45.63% and 56.68% in power consumption, VM migration count, and SLA violations, respectively.

The key to maintaining high standards of quality and power conservation of physical machines in data centers lies in efficient consolidation of virtual machines (VMs). Several schemes have been proposed for this purpose; and these include online migration and VM placement - which can offer the best in terms of resource utilization. The consolidation process can be made effective by finding “opportunities” to migrate VMs as well approximating the resource utilization for the VM placement. An inefficient placement scheme, however, will lead to a substantial overloading of physical machines. This proposed VM placement scheme uses correlation coefficient and predicted future requirements of computing resources to accurately compute the value/s of variable, and has been termed LIFE - Lowest Interdependence Factor Exponent. This variable shows the extent to which a VM can be associated with a target physical machine. Higher value of LIFE will correspondingly result in a larger impact factor influencing the performance of existing VMs whenever a VM is selected for migration to a target machine. To minimize performance degradation, migration of a VM to a target machine will only take place if it is found to correspond with a value of LIFE that is found to be the lowest. Intensive experiments show that the proposed scheme offers better performance attributes over Minimum Correlation Coefficient (MCC) and Power Aware Best Fit Decreasing (PABFD) schemes measured in terms of the following metrics: power consumption by 44.08% and 27.52%, SLA violation by 50.90% and 19.53% and number of VM migration by 52.91% and 9.66% respectively.

Nowadays, cloud computing has emerged as the most popular computing platform due to its ability to reduce the operating costs. A sizable chunk of the operating costs is composed of power consumption of the cloud data center alone. It has been observed that improperly sized virtual machines result in comparatively higher power consumption. Although efforts have been made to address the issue of high power consumption, still efficient resource allocation in a cloud environment continues to pose a significant challenge. The proposed design is based on the concept of clustering tasks based on computing resource requirements, and then distributing the tasks among appropriately sized virtual machines on the basis of computing resources. The current paper evaluates the proposed model against representational techniques in the field. A benchmark dataset from Google cloud trace is used for the evaluation. Empirical results analysis has shown that the proposed model offers significant advantages over the existing schemes in so far as load balancing of active physical servers is concerned in terms of defined performance metrics.

Scientific workflows are increasingly containerised, which requires rethinking central processing unit (CPU) sharing policies to accommodate different workload types. However, container engines running scientific workflows struggle to share the CPU fairly, as workload characteristics are not taken into account. This paper proposes a sharing policy called the Adaptive Completely Fair Scheduling policy (adCFS), which considers the future state of CPU usage and proactively shares CPU cycles between various containers based on their corresponding workload metrics (e.g., CPU usage, task runtime, #tasks). adCFS estimates the weight of workload characteristics and redistributes the CPU based on the corresponding weights. The Markov chain model is used to predict CPU state use, and the adCFS policy is triggered to dynamically allocate containers to the proper CPU portions. Experimental results show enhanced container CPU response time for those containers that run heavy and large jobs: these display 12% faster response time compared with the default CFS (Completely Fair Scheduler). adCFS therefore enhances CFS by considering workload metrics, which leads to the CPU being shared fairly when it is fully used.