BRAHMA(+): A Framework for Resource Scaling of Streaming and ASAP Time-Varying Workflows

Automatic scaling of complex software-as-a-service application workflows is one of the most important problems concerning resource management in clouds. In this paper, we study the automatic workflow resource scaling problem for streaming and ASAP workflows, and its time-varying variant where the workflow resource requirements change over time. Service components of streaming workflows execute concurrently while those of ASAP workflows execute sequentially. We propose an intelligent framework, BRAHMA(+), which possesses the capability to learn the workflow behavior and construct a knowledge base that serves as its decision making engine. The proposed resource provisioning algorithms leverage this learned information curated in the knowledge base to perform informed and intelligent scaling decisions. Additionally, BRAHMA(+) employs the use of online-learning strategies to keep the knowledge base up-to-date, thereby accommodating the changes in the workflow resource requirements over time. We evaluate the proposed algorithms using CloudSim simulations. Results on streaming and ASAP workflows, with both static and time-varying resource requirements show that the proposed algorithms are effective and produce good cost-quality trade-offs. The proactive and hybrid algorithms meet the service level agreements and restrict deadline violations to a small fraction (3%-5% in the considered scenarios), while only suffering a marginal increase in average cost per component compared to the described baseline algorithms

Design and evaluation of automatic workflow scaling algorithms for multi-tenant SaaS

Current Cloud software development efforts to come up with novel Software-as-a-Service (SaaS) applications are, just like traditional software development, usually no longer built from scratch. Instead more and more Cloud developers are opting to use multiple existing components and integrate them in their application workflow. Scaling the resulting application up or down, depending on user/tenant load, in order to keep the SLA, no longer becomes an issue of scaling resources for a single service, rather results in a complex problem of scaling all individual service endpoints in the workflow, depending on their monitored runtime behavior. In this paper, we propose and evaluate algorithms through CloudSim for automatic and runtime scaling of such multi-tenant SaaS workflows. Our results on time-varying workloads show that the proposed algorithms are effective and produce the best cost-quality trade-off while keeping Service Level Agreements (SLAs) in line. Empirically, the proactive algorithm with careful parameter tuning always meets the SLAs while only suffering a marginal increase in average cost per service component of approximate to 5-8% over our baseline passive algorithm, which, although provides the least cost, suffers from prolonged violation of service component SLAs

BRAHMA : an intelligent framework for automated scaling of streaming and deadline-critical workflows

The prevalent use of multi-component, multi-tenant models for building novel Software-as-a-Service (SaaS) applications has resulted in wide-spread research on automatic scaling of the resultant complex application workflows. In this paper, we propose a holistic solution to Automatic Workflow Scaling under the combined presence of Streaming and Deadline-critical workflows, called AWS-SD. To solve the AWS-SD problem, we propose a framework BRAHMA, that learns workflow behavior to build a knowledge-base and leverages this info to perform intelligent automated scaling decisions. We propose and evaluate different resource provisioning algorithms through CloudSim. Our results on time-varying workloads show that the proposed algorithms are effective and produce good cost-quality trade-offs while preventing deadline violations. Empirically, the proposed hybrid algorithm combining learning and monitoring, is able to restrict deadline violations to a small fraction (3-5%), while only suffering a marginal increase in average cost per component of 1-2% over our baseline naive algorithm, which provides the least costly provisioning but suffers from a large number (35-45%) of deadline violations

UnifyDR: A Generic Framework for Unifying Data and Replica Placement

The advent of (big) data management applications operating at Cloud scale has led to extensive research on the data placement problem. The key objective of data placement is to obtain a partitioning (possibly allowing for replicas) of a set of data-items into distributed nodes that minimizes the overall network communication cost. Although replication is intrinsic to data placement, it has seldom been studied in combination with the latter. On the contrary, most of the existing solutions treat them as two independent problems, and employ a two-phase approach: (1) data placement, followed by (2) replica placement. We address this by proposing a new paradigm, CDR , with the objective of c ombining d ata and r eplica placement as a single joint optimization problem. Specifically, we study two variants of the CDR problem: (1) CDR-Single , where the objective is to minimize the communication cost alone, and (2) CDR-Multi , which performs a multi-objective optimization to also minimize traffic and storage costs. To unify data and replica placement, we propose a generic framework called UnifyDR , which leverages overlapping correlation clustering to assign a data-item to multiple nodes, thereby facilitating data and replica placement to be performed jointly. We establish the generic nature of UnifyDR by portraying its ability to address the CDR problem in two real-world use-cases, that of join-intensive online analytical processing (OLAP) queries and a location-based online social network (OSN) service. The effectiveness and scalability of UnifyDR are showcased by experiments performed on data generated using the TPC-DS benchmark and a trace of the Gowalla OSN for the OLAP queries and OSN service use-case, respectively. Empirically, the presented approach obtains an improvement of approximately 35% in terms of the evaluated metrics and a speed-up of 8 times in comparison to state-of-the-art techniques.This work was supported by the Agentschap Innoveren & Ondernemen (VLAIO) Strategic Fundamental Research (SBO) under Grant 150038 (DiSSeCt)

Unifying data and replica placement for data-intensive services in geographically distributed clouds

The increased reliance of data management applications on cloud computing technologies has rendered research in identifying solutions to the data placement problem to be of paramount importance. The objective of the classical data placement problem is to optimally partition, while also allowing for replication, the set of data-items into distributed data centers to minimize the overall network communication cost. Despite significant advancement in data placement research, replica placement has seldom been studied in unison with data placement. More specifically, most of the existing solutions employ a two-phase approach: 1) data placement, followed by 2) replication. Replication should however be seen as an integral part of data placement, and should be studied as a joint optimization problem with the latter. In this paper, we propose a unified paradigm of data placement, called CPR, which combines data placement and replication of data-intensive services into geographically distributed clouds as a joint optimization problem. Underneath CPR, lies an overlapping correlation clustering algorithm capable of assigning a data-item to multiple data centers, thereby enabling us to jointly solve data placement and replication. Experiments on a real-world trace-based online social network dataset show that CPR is effective and scalable. Empirically, it is approximate to 35% better in efficacy on the evaluated metrics, while being up to 8 times faster in execution time when compared to state-of-the-art techniques

Electronic structure of Pr2MnNiO6 from x-ray photoemission, absorption and density functional theory

The electronic structure of double perovskite Pr2MnNiO6 is studied using core x-ray photoelectron spectroscopy and x-ray absorption spectroscopy. The 2p x-ray absorption spectra show that Mn and Ni are in 2+ and 4+ states respectively. Using charge transfer multiplet analysis of Ni and Mn 2p XPS spectra, we find charge transfer energies {\Delta} of 3.5 and 2.5 eV for Ni and Mn respectively. The ground state of Ni2+ and Mn4+ reveal a higher d electron count of 8.21 and 3.38 respectively as compared to the atomic values of 8.00 and 3.00 respectively thereby indicating the covalent nature of the system. The O 1s edge absorption spectra reveal a band gap of 0.9 eV which is comparable to the value obtained from first principle calculations for U-J >= 2 eV. The density of states clearly reveal a strong p-d type charge transfer character of the system, with band gap proportional to average charge transfer energy of Ni2+ and Mn4+ ions.Comment: 18 pages, 9 figure