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1. Archipelago: A Scalable Low-Latency Serverless Platform

   Singhvi, A ; Houck, K ; Balasubramanian, A ; Shaikh, Mohammed S ; Venkataraman, S ; Akella, A ( November 2019 , ArXivorg)

   The increased use of micro-services to build web applications has spurred the rapid growth of Function-as-a-Service (FaaS) or serverless computing platforms. While FaaS simplifies provisioning and scaling for application developers, it introduces new challenges in resource management that need to be handled by the cloud provider. Our analysis of popular serverless workloads indicates that schedulers need to handle functions that are very short-lived, have unpredictable arrival patterns, and require expensive setup of sandboxes. The challenge of running a large number of such functions in a multi-tenant cluster makes existing scheduling frameworks unsuitable. We present Archipelago, a platform that enables low latency request execution in a multi-tenant serverless setting. Archipelago views each application as a DAG of functions, and every DAG in associated with a latency deadline. Archipelago achieves its per-DAG request latency goals by: (1) partitioning a given cluster into a number of smaller worker pools, and associating each pool with a semi-global scheduler (SGS), (2) using a latency-aware scheduler within each SGS along with proactive sandbox allocation to reduce overheads, and (3) using a load balancing layer to route requests for different DAGs to the appropriate SGS, and automatically scale the number of SGSs per DAG. Our testbed resultsmore »show that Archipelago meets the latency deadline for more than 99% of realistic application request workloads, and reduces tail latencies by up to 36X compared to state-of-the-art serverless platforms.« less
2. A Theory of Auto-Scaling for Resource Reservation in Cloud Services

   https://doi.org/10.1287/stsy.2021.0091  

   Psychas, Konstantinos ; Ghaderi, Javad ( February 2022 , Stochastic Systems)

   We consider a distributed server system consisting of a large number of servers, each with limited capacity on multiple resources (CPU, memory, etc.). Jobs with different rewards arrive over time and require certain amounts of resources for the duration of their service. When a job arrives, the system must decide whether to admit it or reject it, and if admitted, in which server to schedule it. The objective is to maximize the expected total reward received by the system. This problem is motivated by control of cloud computing clusters, in which jobs are requests for virtual machines (VMs) or containers that reserve resources for various services, and rewards represent service priority of requests or price paid per time unit of service. We study this problem in an asymptotic regime where the number of servers and jobs’ arrival rates scale by a factor L, as L becomes large. We propose a resource reservation policy that asymptotically achieves at least 1/2, and under certain monotone property on jobs’ rewards and resources, at least [Formula: see text] of the optimal expected reward. The policy automatically scales the number of VM slots for each job type as the demand changes and decides in whichmore »servers the slots should be created in advance, without the knowledge of traffic rates.« less
3. Stealthy-Shutdown: Practical Remote Power Attacks in Multi - Tenant FPGAs

   https://doi.org/10.1109/ICCD50377.2020.00097  

   Luo, Yukui ; Gongye, Cheng ; Ren, Shaolei ; Fei, Yunsi ; Xu, Xiaolin ( October 2020 , IEEE International Conference on Computer Design: VLSI in Computers and Processors)

   With the deployment of artificial intelligent (AI) algorithms in a large variety of applications, there creates an increasing need for high-performance computing capabilities. As a result, different hardware platforms have been utilized for acceleration purposes. Among these hardware-based accelerators, the field-programmable gate arrays (FPGAs) have gained a lot of attention due to their re-programmable characteristics, which provide customized control logic and computing operators. For example, FPGAs have recently been adopted for on-demand cloud services by the leading cloud providers like Amazon and Microsoft, providing acceleration for various compute-intensive tasks. While the co-residency of multiple tenants on a cloud FPGA chip increases the efficiency of resource utilization, it also creates unique attack surfaces that are under-explored. In this paper, we exploit the vulnerability associated with the shared power distribution network on cloud FPGAs. We present a stealthy power attack that can be remotely launched by a malicious tenant, shutting down the entire chip and resulting in denial-of-service for other co-located benign tenants. Specifically, we propose stealthy-shutdown: a well-timed power attack that can be implemented in two steps: (1) an attacker monitors the realtime FPGA power-consumption detected by ring-oscillator-based voltage sensors, and (2) when capturing high power-consuming moments, i.e., the power consumptionmore »by other tenants is above a certain threshold, she/he injects a well-timed power load to shut down the FPGA system. Note that in the proposed attack strategy, the power load injected by the attacker only accounts for a small portion of the overall power consumption; therefore, such attack strategy remains stealthy to the cloud FPGA operator. We successfully implement and validate the proposed attack on three FPGA evaluation kits with running real-world applications. The proposed attack results in a stealthy-shutdown, demonstrating severe security concerns of co-tenancy on cloud FPGAs. We also offer two countermeasures that can mitigate such power attacks.« less
4. Threshold Based Load Balancing Algorithm in Cloud Computing

   https://doi.org/10.1109/JCC56315.2022.00011  

   Chowdhury, Shusmoy ; Katangur, Ajay ( August 2022 , 2022 IEEE International Conference on Joint Cloud Computing (JCC))

   Cloud computing has become an emerging trend for the software industry with the requirement of large infrastructure and resources. The future success of cloud computing depends on the effectiveness of instantiation of the infrastructure and utilization of available resources. Load Balancing ensures the fulfillment of these conditions to improve the cloud environment for the users. Load Balancing dynamically distributes the workload among the nodes in such a way that no single resource is either overwhelmed with tasks or underutilized. In this paper we propose a threshold based load balancing algorithm to ensure the equal distribution of the workload among the nodes. The main objective of the algorithms is to stop the VMs in the cloud being overloaded with tasks or being idle for lack allocation of tasks, when there are active tasks. We have simulated our proposed algorithm in the Cloudanalyst simulator with real world data scenarios. Simulation results shows that our proposed threshold based algorithm can provide a better response time for the task/requests and data processing time for the datacenters compared to the existing algorithms such as First Come First Serve (FCFS), Round Robin(RR) and Equally Spread Current Execution Load Balancing algorithm(ESCELB).
5. Optimal Load Balancing with Locality Constraints

   https://doi.org/10.1145/3428330  

   Weng, Wentao ; Zhou, Xingyu ; Srikant, R. ( November 2020 , Proceedings of the ACM on Measurement and Analysis of Computing Systems)

   Applications in cloud platforms motivate the study of efficient load balancing under job-server constraints and server heterogeneity. In this paper, we study load balancing on a bipartite graph where left nodes correspond to job types and right nodes correspond to servers, with each edge indicating that a job type can be served by a server. Thus edges represent locality constraints, i.e., an arbitrary job can only be served at servers which contain certain data and/or machine learning (ML) models. Servers in this system can have heterogeneous service rates. In this setting, we investigate the performance of two policies named Join-the-Fastest-of-the-Shortest-Queue (JFSQ) and Join-the-Fastest-of-the-Idle-Queue (JFIQ), which are simple variants of Join-the-Shortest-Queue and Join-the-Idle-Queue, where ties are broken in favor of the fastest servers. Under a "well-connected'' graph condition, we show that JFSQ and JFIQ are asymptotically optimal in the mean response time when the number of servers goes to infinity. In addition to asymptotic optimality, we also obtain upper bounds on the mean response time for finite-size systems. We further show that the well-connectedness condition can be satisfied by a random bipartite graph construction with relatively sparse connectivity.