Edge computing promises a considerable reduction in latency and data volume by placing edge frameworks near the data source. Stream processing framework is a critical use case for edge computing, presenting in-situ data processing and low latency. However, careful CPU resource control is more important than cloud computing to provide effective multi-tenancy in resource-constrained edge computing. In this work, we investigate practical ways of controlling CPU resources of stream processing frameworks. Evaluation results from the different stream processor parallelism and cgroup parameters give a clear direction of designing an essential controller for stream processing frameworks in edge computing.

In large-scale distributed systems, such as clouds, failures are rather the norm than the exception. These failures include job failures, server failures, network outage and power failure. Among them, server failures are most common. With the wide adoption of cloud computing, the impact of server failures in clouds is far greater than that in traditional computer clusters as jobs of different tenants are often co-located (multi-tenancy). In this paper, we address the problem of robust scheduling, with realistic failure modeling, to minimize such impact on the execution of (co-located) jobs. To this end, we develop four online failure-aware (FA) scheduling algorithms, FAFF-WJ, FAFF-FC, FABF-WJ and FABF-FC, considering the availability and reliability of servers. In particular, FF (First-Fit) and BF (Best-Fit) indicate how the availability of servers is checked while WJ (Waiting Job) and FC (Failure Count) differ primarily in whether the reliability is measured from job's perspective or server's perspective. All four algorithms are designed essentially by combining these availability and reliability check methods. We evaluate our scheduling algorithms with failures generated based on our failure modeling of six real-world server failure traces. Our evaluation results show the effectiveness of our scheduling algorithms in robust job execution, with respect to both performance and cost.

Serverless computing has emerged as a new cloud computing execution model that liberates users and application developers from explicitly managing `physical' resources, leaving such a resource management burden to service providers. In this article, we study the problem of resource allocation for multi-tenant serverless computing platforms explicitly taking into account workload fluctuations including sudden surges. In particular, we investigate different root causes of performance degradation in these platforms where tenants (their applications) have different workload characteristics. To this end, we develop a fine-grained CPU cap control solution as a resource manager that dynamically adjusts CPU usage limit (or CPU cap) concerning applications with same/similar performance requirements, i.e., application groups. The adjustment of CPU caps applies primarily to co-located worker processes of serverless computing platforms to minimize resource contention, which is the major source of performance degradation. The actual adjustment decisions are made based on performance metrics (e.g., throttled time and queue length) using a group-aware scheduling algorithm. The extensive experimental results performed in our local cluster confirm that the proposed resource manager can effectively eliminate the burden of explicit reservation of computing capacity, even when fluctuations and sudden surges in the incoming workload exist. We measure the robustness of the proposed resource manager by comparing it with several heuristics which extensively used in practice, including the enhanced version of round robin and the least length queue scheduling policies, under various workload intensities driven by real-world scenarios. Notably, our resource manager outperforms other heuristics by decreasing skewness and average response time up to 44 and 94 percent, respectively, while it does not over-use the CPU resources.

Distributed (computing) systems come in various sizes and scale. They range from a single workstation computer with several processors, a cluster of compute nodes (servers) to a federation of geographically distributed data centres with millions of servers. Job scheduling is a fundamental aspect for data centre efficiency. In this paper, we present ds-viz as a visualisation aid for ds-sim, a recently developed distributed systems simulator. In particular, ds-viz significantly helps leverage the evaluation and analysis of scheduling algorithms that ds-sim facilitates to design. We show the effectiveness of these tools with some examples.

Workload surges are a serious hindrance to per-formance of even high-throughput key-value data stores, such as Cassandra, MongoDB, and more recently Aerospike. In this paper, we present a decentralized admission controller for high-throughput key-value data stores. The proposed controller dynamically regulates the release time of incoming requests explicitly taking into account different Quality of Service (QoS) classes. In particular, an instance of such controller is assigned to each client for its autonomous admission control specific to the client's QoS requirements. These controllers operate in a decentralized manner with only local performance metrics, response time and queue waiting time. Despite the use of such "minimal" run-time state information, our decentralized admission controller is capable of coping with workload surges respecting QoS requirements. The performance evaluation is carried out by comparing the proposed admission controller with the default scheduling policy of Aerospike, in a testbed cluster under various workload intensity rates. Experimental results confirm that the proposed controller improves QoS satisfaction in terms of end-to-end response time by nearly 12 times, on average, compared with that of Aerospike's, in high-rate workload. Results also show decreases of the average and standard deviation of latency up to 31% and 50%, respectively, during workload surges (peak load) in high-rate workload.

Lambda platform is a new concept based on an event-driven server-less computation that empowers application developers to build scalable enterprise software in a virtualized environment without provisioning or managing any physical servers (a server-less solution). In reality, however, devising an effective consolidation method to host multiple Lambda functions into a single machine is challenging. The existing simple resource allocation algorithms, such as the round-robin policy used in many commercial server-less systems, suffer from lack of responsiveness to a sudden surge in the incoming workload. This will result in an unsatisfactory performance degradation that is directly experienced by the end-user of a Lambda application. In this paper, we address the problem of CPU cap management in a Lambda platform for ensuring different QoS enforcement levels in a platform with shared resources, in case of fluctuations and sudden surges in the incoming workload requests. To this end, we present a closed-loop (feedback-based) CPU cap controller, which fulfills the QoS levels enforced by the application owners. The controller adjusts the number of working threads per QoS class and dispatches the outstanding Lambda functions along with the associated events to the most appropriate working thread. The proposed solution reduces the QoS violations by an average of 6.36 times compared to the round-robin policy. It can also maintain the end-to-end response time of applications belonging to the highest priority QoS class close to the target set-point while decreasing the overall response time by up to 52%.

As technologies and services that leverage cloud computing evolve, more and more businesses or individuals are using them. However, as users increasingly use and store personal information in cloud storage, research on privacy protection models in the cloud environment is becoming more important. A self-destructing scheme has been proposed to prevent the decryption of encrypted user data after a certain period of time using a DHT network. However, the existing privacy protection model does not mention the method of setting the threshold value considering the availability and security of the data. Therefore, in this paper, we propose an optimal threshold finding method considering both data availability and security of privacy protection model by applying regression analysis.

Recent distributed key-value data stores, such as Aerospike are getting the momentum with ever-increasing need for large-scale real-time data processing. While these data stores can provide significantly improved performance, they still struggle to meet Quality of Service (QoS) during workload surges. In this paper, we address the problem of QoS-aware resource allocation for burst workloads in key-value data stores. To this end, we design a resource allocation controller, which enables each application to independently regulate the releases of its requests taking into account QoS. In particular, the proposed controller monitors the actual performance metrics of the target system and dynamically releases requests from a buffer owned by each application accordingly. We have implemented the proposed controller in an Aerospike cluster for our performance evaluation. Experiments have been conducted with various workload intensities (with up to 36,180 write operations per second) in comparison with the default Aerospike policy. Experimental results confirm that the proposed controller decreases the overall average latency up to 41% on high-rate workload while maintaining the QoS of high priority applications.